ContextCore | Reach2 Context Exchange Standard (CXS)

Remember
Every Session.

ContextCore turns your AI Agents conversation history into a searchable, MCP-queryable, composable memory layer for AI-assisted development.

Explore Features Install from GitHub
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AI context is THE artifact of our age.

You have a brilliant debugging session with Claude at 2 AM, make critical architectural decisions with Copilot during a sprint, or work through a complex refactor with Cursor over multiple days — and all of that context is scattered across IDEs with very poor search tooling.

ContextCore makes it permanent, searchable, and reusable.

See It in Action

The Six Pillars

Built by engineers for engineers. No fluff, just a solid memory layer.

Multi-Harness Ingestion

Reads chat history from Claude Code, Cursor, Open Code, Kiro, and VS Code Copilot. Normalizes everything into a single unified format regardless of IDE.

Ingestion pipeline

MCP Server

Transforms storage into a live memory layer. Allow any MCP-capable LLM to search your archive and build on prior reasoning automatically.

MCP integration

Hybrid Search

Fuzzy lexical search (Fuse.js) combined with semantic vector search (Qdrant). Find conversations by exact phrases or underlying meaning.

Hybrid search

Agent Builder

Compose and edit reusable AI agents from indexed architecture docs, upgrade notes, and code files directly in the visualizer UI.

Agent Builder

Interactive Visualization

A D3-powered zoomable card map. Explore your history spatially, save favorites, and spin up AI agents directly from your past chats & indexed project knowledge.

Skills & Memory Manager

Package repeatable engineering workflows into reusable skills and memory assets so future agents can apply your team's proven thinking patterns.

Planned

Workflow Examples

  • Continue Tasks

    Pick up exactly where you left off.

    Load prior session transcripts via MCP to give your agent full context of what was tried, what worked, and what was decided months ago.

  • Debug Regressions

    Understand historical architectural decisions.

    When an LLM encounters a regression, it can search conversation history to understand what changed, when, and why using the rationale captured in previous chats.

  • Search by Concept

    Unified cross-IDE search.

    A search for "authentication" returns results from Claude Code, Cursor, Kiro, and VS Code sessions equally, ranked by relevance regardless of origin.

  • Agent Builder showcase
  • D3 Conversation Viewer threads
  • D3 Conversation Viewer messages

    Agent Builder

    Compose AI agents directly from the visualizer UI using your indexed knowledge base.

    What Makes It Unique

    Cross-Harness Unification

    No other system reads from JSONL streams, SQLite databases, and incremental JSON patches, normalizing all of it into the same searchable model.

    AI-Accessible Memory

    Not just storage — it's a feedback loop where AI agents build on each other's work via MCP tools, resources, and pre-built prompts.

    Real-time Ingestion

    File watcher detects changes to harness source files as they happen. New conversations appear in the system within seconds, fully indexed.

    Technical Credibility

    Built for engineers, using a modern stack designed for speed and extensibility.

    • Runtime: Bun with native SQLite via bun:sqlite
    • Database: SQLite (in-memory or on-disk WAL mode)
    • Search: Fuse.js (lexical) + Qdrant (vector embeddings)
    • Frontend: React + Vite + D3.js
    • Protocol: Model Context Protocol (MCP)

    Quick Start

    Spin up the orchestrator and visualizer locally:

    # Install dependencies
cd server && bun install
cd ../visualizer && bun install

# Setup
cd ../server && bun setup

# Start the server (ingestion + API + MCP)
cd server && bun run dev

# Start the visualizer
cd visualizer && bun run dev
    Clone the Repository